Métis Student Self-Identification in Ontario's K-12 Schools: Education Policy and Parents, Families, and Communities.
Bibliographic record
Abstract
The mandate for school boards to develop self-identification policies for First Nation, Metis, and Inuit students is part of the 2007 Ministry of Education’s Ontario First Nation, Metis and Inuit Education Policy Framework . In this paper, we share findings from a larger study on the Framework that examines Metis student self-identification processes and assesses barriers, challenges, opportunities, and best practices. We draw on themes from a literature review concerning Metis education and we examine data from an online survey and key interviews with school administrators responsible for initiatives to support Metis students ’ self-identification. The survey and interviews took place in the winter of 2011. We find that, for the self-identification policy to be effective, teachers, administrators, and support staff (clerks, receptionists, secretaries, and teaching/educational assistants) must build a school climate that welcomes Metis learners and parents, families, and communities and affirms their historical and contemporary values in practice. This way, students and their families may feel comfortable to identify as Metis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".